ECCO2 6-month (April-September 2006) Report by bigmekahlo


									                          ECCO2 progress report, 2008
                            John Marshall, December 2008
                          Massachusetts Institute of Technology

This report describes our third year of activities in the ECCO2 project in the areas of:
global ocean state estimation (see Section 1), computation and model development
(Section 2), state estimation methodologies (Section 3), and science applications (Section
4). Before going on to a more detailed description of our various activities, we briefly
summarize where we are and where we are going.

ECCO2‟s goal is to produce global estimates of the evolving ocean circulation and sea-
ice state at eddy-resolving scales. This is a very difficult problem because, first, the
pervasiveness of the ocean‟s geostrophic eddy field makes it so difficult to observe and
model and, second, prior to the Argo period (2003 onwards) there was a paucity of in-situ
observations with which to constrain the interior of ocean models.

A preliminary global, eddy-resolving optimization has already been achieved at JPL
based on a Green‟s function methodology (see Section 1). The approach is very effective
at removing gross drifts of the model from the data, but fails to closely fit the data. At
MIT, meanwhile, the efficacy of fitting an eddying model of the Southern Ocean to data
using adjoint techniques has been explored. The adjoint approach allows the adjustment
of many more control parameters. The model is fit to the data year by year in the data-
rich Argo period with very encouraging results.

                Participants and organization of the ECCO2 project
As we complete year three of our project, then, we have drawn together the best aspects
of the Green‟s function and adjoint approaches and, in a close JPL-MIT collaborative
effort, are now attempting to constrain the eddying cube of Menemenlis et al. (2005a)
using the adjoint methods. As described in Section 5, we are focusing on the recent,
Argo-rich period and are fitting the model to the data year by year.

In summary we believe we are on track to achieve our ultimate goal of an eddying global
solution in the presence of ice, which closely fits the available data.

1. Global State Estimation
Green‟s function optimization

The specific objective of our first global eddying ECCO2 optimization was to reduce
large-scale biases and drifts of the model relative to observations, such as a global warm
bias of the upper ocean, hydrographic properties of intermediate and deep waters, sea ice
thickness, velocity, and extent, and key oceanic transports. The estimates are obtained by
least squares fit of a global full-depth-ocean and sea-ice configuration of the
Massachusetts Institute of Technology general circulation model (MITgcm; Marshall et
al., 1997) to the available satellite and in-situ data.

This global ECCO2 solution has been obtained using the model configuration of
Menemenlis et al. (2005a) employing a Green‟s function approach. A cube-sphere grid
projection (Figure 1) is employed, which permits relatively even grid spacing throughout
the domain and avoids polar singularities (Adcroft et al., 2004). Each face of the cube
comprises 510 by 510 grid cells yielding a mean horizontal grid spacing of 18 km,
adequate to capture, but not fully represent, baroclinic instability of the large-scale
currents. The model has 50 vertical levels ranging in thickness from 10 m near the
surface to approximately 450 m at a maximum model depth of 6150 m. The partial-cell
formulation of Adcroft et al. (1997) is used, which permits accurate representation of the
bathymetry. The model is integrated in a volume-conserving configuration using a finite
volume discretization with C-grid staggering of the prognostic variables. The ocean
model is coupled to a sea-ice model that computes ice thickness, ice concentration, and
snow cover as described in Zhang et al. (1998) and that simulates a viscous-plastic
rheology using an efficient parallel implementation of the Zhang and Hibler (1997)
solver. Inclusion of sea-ice provides for more realistic surface boundary conditions in
Polar Regions and allows the system to be constrained by polar satellite observations.
The sea-ice model also permits estimation of the time-evolving sea-ice thickness

The first high-resolution global-ocean and sea-ice data synthesis was obtained for the
period 1992-2002 by calibrating a small number of control variables using a Green‟s
function approach (Menemenlis et al., 2005b). The control parameters include initial
temperature and salinity conditions, atmospheric surface boundary conditions,
background vertical diffusivity, critical Richardson numbers for the Large et al. (1994)
KPP scheme, air-ocean, ice-ocean, air-ice drag coefficients, ice/ocean/snow albedo
coefficients, bottom drag, and vertical viscosity. Data constraints include sea level
anomaly from altimeter data, time-mean sea level from Maximenko and Niiler (2005),
temperature and salinity profiles from WOCE, TAO, ARGO, XBT, etc., sea ice
concentration from passive microwave data, and sea ice thickness from ULS. A detailed
evaluation of this solution and some early science applications were presented at the
Ocean Sciences Meeting in Orlando, Florida, on March 2-7, 2008. The presentations are
available at Many of the results
presented at that meeting are now being prepared for publication.

A large complement of high-frequency and high-resolution diagnostics has been saved
and are made available to the scientific community via ftp and OPeNDAP servers at For the global cubed-sphere grid configuration, in addition to the
optimized solution, upwards of 80 forward model sensitivity experiments are available.
These experiments, which were used for the Green‟s function optimization, explore the
model‟s response to different surface boundary conditions, initial conditions, horizontal
and vertical mixing parameters, sea-ice model parameters, and to the addition of various
sub-grid scale parameterizations. We have also carried out a longer, 1979–present,
forward integration using the optimized model parameters. The available diagnostics
include surface fluxes, sea surface height, bottom pressure, mixed and mixing layer
depths, sea-ice thickness, concentration, salinity, and velocity, ocean temperature,
salinity, density, and velocity, and eddy transports of mass, temperature, and salt. A large
portion of these diagnostics (~100 TB) is readily available online via ftp, http, and
OPeNDAP servers. The complete diagnostics are stored on tapes at the NASA Advanced
Supercomputing (NAS) and are made available upon request.

Despite significant large-scale improvements in this Green‟s function solution relative to
the baseline integration, there remain many problematic aspects such as the
representation of Mode Water formation processes, boundary current separation, Arctic
sea ice distribution and hydrography, and ice-shelf-ocean interactions, which are the
subject of ongoing work (see Section 5).

2. Computation and model development

MITgcm physics and dynamics

Campin, Marshall, and Ferreira (2008), show how the problem of „levitating ice‟ in z-
coordinate ocean models can be solved through use of a rescaled vertical coordinate “z∗”
in z-coordinate models that allows one to follow undulations in the free surface under
sea-ice loading. In particular, the adoption of “z∗” avoids the difficult issue of vanishing
surface levels under thick ice. Details of the implementation within MITgcm were sorted
out in the context of the global eddy resolving ECCO2 model.

J-M Campin implemented his representation of overflows in the MITgcm (Campin and
Goose, 1999). It is currently being tested in regional and global ECCO2 configurations.
Calculations have finally been completed in our development of a „super-
parameterization approach‟ to subgridscale mixing in ocean models, in which non-
hydrostatic submodels are embedded in to a large-scale model. The trial problem has
been open-ocean deep convection, but the approach is very general and can be applied to
all kinds of subgridscale physics. The software has been developed under ESMF. A paper
is in preparation.

Sea-ice modeling

The MITgcm sea-ice model has been a major development focus and now includes (i) a
reformulation of the dynamics solver on an Arakawa C-grid consistent with that used for
the ocean dynamics, (ii) the option to use the Elastic Visco-Plastic Rheology, (iii)
addition of prognostic state variables for snow and salinity, (iv) many improvements to
the thermodynamics, and (v) the option to specify sea ice open boundary conditions
(Losch, Menemenlis, Heimbach, Campin, and Hill, in preparation).

Nguyen et al. (2008) investigated the reasons why coupled ocean and sea ice models in
the Arctic tend to misrepresent the upper ocean stratification. Specifically, results from
the Arctic Ocean Model Intercomparison Project (AOMIP) showed that participating
ocean models consistently failed to either produce and/or maintain the cold halocline
layer at the 50–200-m depth. Without a cold halocline, excess heat flux from the warm
Atlantic water source at greater depths can inhibit production of realistic sea ice extent
and thickness. To address this problem, a new sub-grid-scale parameterization of salt
plumes was developed, resulting in a considerably more realistic representation of the
cold halocline in the Arctic Ocean (Figure 2).

Another area of large model errors and uncertainties is the representation of ice shelf-
ocean processes. The addition of freshwater from the sub-ice-shelf cavities leads to an
increase in sea ice thickness. It also leads to a reduction of dense water masses on the
continental shelf. M. Schodlok is using the ice shelf model of Losch (2008) to study ice
shelf-ocean interactions and to improve the representation of ice shelf processes in a
regional Southern Ocean domain. This work is the first high-resolution model solution of
all ice shelves of the Antarctic continent and it suggests higher values of circumpolar
freshwater fluxes than previously estimated (Figure 3). The global impact of this
freshwater flux, e.g., on the meridional overturning, remains to be investigated.

An important objective of the MITgcm ocean and sea-ice model development effort has
been to provide the capability for automatic generation of the adjoint model from up-to-
date versions of the full GCM, which is invaluable for ocean state estimation. An adjoint
of the full-fledged dynamic/thermodynamic MITgcm sea-ice component has now been
achieved. The coupled ocean/sea-ice adjoint yields stable and physically meaningful
adjoint sensitivities or Lagrange multipliers. It has been applied to a regional Arctic
domain, which was derived from the Arctic face of the global ocean/sea-ice ECCO2
cubed-sphere configuration, but coarsened from an 18 km to 36 km horizontal resolution
to be computationally more tractable (the system runs efficiently on 80 processors).
Results are described in Heimbach (2008).

Regional/Nested modeling

Several ongoing ECCO2 investigations (See Section 4) are occurring in regional domains
that are carved out and/or that obtain boundary conditions from the global integration. A
versatile grid generation program for domain decomposed GFD has been developed (Hill
et al. 2008) which enables, for example, to flexibly patch in higher-resolution regional
models in to a global domain. A set of software tools is available to facilitate the set-up of
these experiments.

Gridding and high-resolution modeling

High-resolution simulations are being used by the ECCO2 project to estimate model
errors (Forget and Wunsch, 2007; Ponte et al., 2007) and to inform sub-grid-scale
parameterizations (Hill et al., 2007; Fox-Kemper and Menemenlis, 2008; Danabasoglu et
al., 2008). A quasi-global simulation with 1/16th-degree horizontal grid spacing was
recently configured on NASA Ames‟ latest supercomputer (Figure 4) and results from
this integration are already being used by L.-L. Fu and by E. Rodriguez to inform the
design of the Surface Water and Ocean Topography (SWOT), a Decadal-Survey-
recommended high-resolution altimeter mission.

3. State estimation methodologies
Eddy-resolving adjoint-based state estimation

A key result of the ECCO2 effort to date has been to demonstrate that an adjoint
approach to ocean state estimation is feasible in the presence of the ocean‟s ubiquitous
eddy field. If the period of time over which the model is fitted to data is short enough and
the density of data is sufficiently large, one can proceed.

M. Mazloff has produced an eddy-permitting state estimate of the Southern ocean at 1/6-
degree horizontal grid spacing, and covering the Argo-rich observation period 2005/2006.
Fig. 5 depicts first guess and residual misfit between observed and modeled SST after 23
iterations. Almost all of the data employed in the first-generation ECCO efforts have
been utilized with major emphasis on the satellite altimetry, the Argo profiles, and
satellite Sea Surface Temperature (SST). Output has now been made available to the
research community through ECCO's data servers. This work was featured in WHOI's
"Oceanus" magazine (Mazloff, 2008a,b). The state estimate is now being used by various
research groups (e.g. R. Abernathey, J. Marshall, and R. Ferrari at MIT, T. Ito, University
of Colorado and L. Talley, SIO/UCSD) to study stirring and mixing in the Southern
Ocean and the dynamics of the meridional overturning circulation in the Southern Ocean.
As reviewed in Section 5, an intensive effort is now underway to carry out a global
adjoint-method optimization on the same grid that was used for the global Green‟s
function minimization described in Section 2.

Progress in the OpenAD tool development

In related work, the ECCO2 project is contributing to the development of an open-source
Automatic Differentiation (AD) tool called OpenAD. Effort during this past year has
gone towards the generation of efficient adjoint code for the MITgcm. After
demonstrating that OpenAD can handle a simplified configuration of the MITgcm we are
now in a position to adjoint a global, coarse-resolution MITgcm configuration (including
the GM/Redi eddy parameterization scheme), which has been a workhorse setup for
various adjoint sensitivity studies. The OpenAD software is now useable for science and
engineering applications. For example, using OpenAD, we have conducted a 100-year
adjoint integration to compute transient sensitivities of Atlantic meridional heat transport.

4. Science applications
Improved error estimates and eddy parameterizations for coarse-resolution ocean
simulations and estimations.

Forget and Wunsch (2007) used hydrographic data and an early ECCO2 simulation to
estimate global hydrographic variability and data weights in oceanic state estimates.
Ponte et al. (2007) used altimeter data and an early ECCO2 simulation for spatial
mapping of time-variable errors in Jason-1 and TOPEX/POSEIDON sea surface height
measurements. ECCO2 high-resolution simulations have also been used to inform the
model parameterization of sub-grid scale processes (Fox-Kemper and Menemenlis, 2008;
Danabasoglu et al., 2008).

Impact of mesoscale eddies on large-scale ocean circulation and its variability.

Fu (2006) used correlation between successive maps of sea-surface height to estimate
eddy propagation characteristics. Similarities and differences between results from
observed and simulated sea-surface height variability improve understanding of model
and data errors and of the underlying physical processes. Volkov and Fu (2008) studied
the dynamics of the Zapiola Anticyclone, which is situated in a highly energetic area of
the ocean, by analyzing the vorticity balance of the anticyclone. This helped to
understand the main physical mechanisms that drive the variability of the anticyclone.
Volkov et al. (2008) used an ECCO2 solution to estimate the eddy-induced meridional
heat transport in the ocean. Their research demonstrates that the variability of the eddy
heat transport is a significant contributor to the variability of the total heat transport.
ECCO2 solutions are also being used to study the impact of eddies on mode water
formation. For example, Maze et al. (2008) and Forget et al. (2008) investigated North
Atlantic subtropical mode water formation while Davis (2008) studied the formation,
evolution, and dispersal of Subtropical Mode Water (STMW) in the North Pacific Ocean.
Study of Polar Oceans

Condron et al. (2008) studied the response of the Arctic freshwater budget to extreme
North Atlantic Oscillation (NAO) forcing. Kwok et al. (2006) used ECCO2 estimates of
Arctic sea surface height variability to estimate contributions of the oceanographic
circulation signal to Ice, Cloud, and land Elevation Satellite (ICESat) retrievals in order
to help interpret ICESat altimetric and reflectivity profiles. Kwok et al. (2008) compared
sea ice results from one of the ECCO2 solutions and from other coupled-ice-ocean
models to observations obtained by the RADARSAT Geophysical Processor System
(RGPS). Figure 6 is from ongoing Arctic Ocean work by A. Nguyen.

Finally, ECCO2 results are being used to supply boundary conditions for regional studies
and to drive biogeochemical, geodetic, acoustic, and electromagnetic models. For
example, Manizza et al. (2008) used ECCO2 results to examine the fate of riverine fluxes
of Dissolved Organic Carbon (DOC) in the Arctic Basin. Dushaw et al. (2008) used
ECCO2 results to design an acoustic array for observing gyre-scale acoustic variability in
the North Atlantic. Glazman and Golubev (2005) used an early ECCO2 simulation to
investigate the spatial and temporal variability of the Earth's magnetic field component
induced by ocean circulation. Figure 7 is from ongoing work by O. Jahn and M. Follows
on self-assembling marine ecosystems.

5. Outlook
To address the shortcomings of the existing ECCO2 global-ocean and sea-ice synthesis,
which was obtained using a Green‟s function approach (Section 1), the following parallel
efforts are underway:

   (i) Improved model physics, for example, of deep water formation over continental
          shelves (Campin and Goose, 1999), of salt plumes rejected by sea ice during
          freezing (Nguyen et al., 2008), of ice shelf-ocean interactions (Losch, 2008),
          of small-scale ice mechanics and thermodynamics (Kwok et al., 2008), and of
          super-parameterizations, as discussed in Section 2.

   (ii) Ultra-high resolution forward integrations are being used to estimate model
           errors and to inform sub-grid-scale parameterizations.

   (iii) Focus on the Argo-rich data period (2003 through to the present) and fitting the
           model to data year-by-year rather than decade by decade thus reducing
           problems of model drift.

   (iv) Improved estimates of initial and boundary conditions using the adjoint method,
          which permits a much larger number of control variables than does the
          Green‟s function approach.
In particular, Mazloff (2008a,b) has demonstrated the feasibility of high-resolution
adjoint-based state estimation on a regional scale. Given the success of this regional,
eddy resolving, adjoint-based state estimation effort and of the sea-ice sensitivity
experiments, we are currently attempting an adjoint-method optimization on the global,
cubed-sphere, ocean and sea-ice model configuration as a way to increase the number of
control variables relative to the existing Green‟s function optimization. Stable adjoint
sensitivities have been computed for close to one year of adjoint integration (Figure 1).
Such sensitivities are the major prerequisite for optimization. Thus, as we move in to year
4 of the project, we fully expect to deliver a first demonstration of a global, eddying
solution constrained using the adjoint method.

Adcroft, A., C. Hill, and J. Marshall, 1997: Representation of topography by shaved cells
in a height coordinate ocean model. Mon. Weather Rev., 125, 2293–2315.

Adcroft, A., J. Campin, C. Hill and J. Marshall, 2004: Implementation of an atmosphere-
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Campin, J.-M., and H. Goosse, 1999: A parameterization of density downsloping flow for
a coarse resolution ocean model in z-coordinate. Tellus, 51A, 412–430.

Campin, J.-M., J. Marshall, and D. Ferreira, 2008: Sea-ice ocean coupling using a
rescaled vertical coordinate z*, Ocean Modeling, 24, 1-14.

Condron, A., P. Winsor, C. Hill and D. Menemenlis, 2008: Response of the Arctic
freshwater budget to extreme NAO forcing. J. Climate, submitted.

Danabasoglu, G., R. Ferrari, and J. McWilliams, 2008: Sensitivity of an ocean general
circulation model to a parameterization of near-surface eddy fluxes. J. Climate, 21, 1192–

Davis, X., 2008: Numerical and theoretical investigations of North Pacific Subtropical
Mode Water with implications to Pacific climate variability. Ph.D. thesis, University of
Rhode Island, Kingston, RI.

Dushaw, B., P. Worcester, R. Andrew, B. Howe, J. Mercer, R. Spindel, B. Cornuelle, M.
Dzieciuch, W. Munk, T. Birdsall, K. Metzger, D. Menemenlis, and C. Wunsch, 2008: A
decade of acoustic thermometry in the North Pacific Ocean: using long-range acoustic
travel times to test gyre-scale temperature variability derived from other observations and
ocean models. J. Geophys. Res., submitted.

Forget, G. and C. Wunsch, 2007: Global hydrographic variability and the data weights in
oceanic state estimates. J. Phys. Oceanogr., 37, 1997–2008.

Forget, G., Maze, G., Buckley, M and Marshall, J. (2008): Quantitative and dynamical
analysis of EDW formation using a model-data synthesis. J. Phys. Oceanogr., submitted.

Fox-Kemper, B., and D. Menemenlis, 2008: Can Large Eddy Simulation Techniques
Improve Mesoscale Rich Ocean Models? Ocean Modeling in an Eddying Regime, ed.
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Fu, L.-L., 2006: Pathways of eddies in the South Atlantic revealed from satellite altimeter
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Glazman, R. and Y. Golubev, 2005: Variability of the ocean-induced magnetic field
predicted at sea surface and at satellite altitudes. J. Geophys. Res., 110, C12011.

Heimbach, P., 2008: The MITgcm/ECCO adjoint modeling infrastructure. CLIVAR
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Hill, C., D. Menemenlis, B. Ciotti, and C. Henze, 2007: Investigating solution
convergence in a global ocean model using a 2048-processor cluster of distributed shared
memory machines. Scientific Programming, 12, 107–115.

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Kwok, R., E. Hunke, W. Maslowski, D. Menemenlis, and J. Zhang, 2008: Variability of
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Manizza, M., M. Follows, S. Dutkiewicz, J. McClelland, D. Menemenlis, C. Hill, and J.
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Maze, G., G. Forget, M. Buckley and J. Marshall, 2008: Using transformation and
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ocean general circulation model. Mon. Weather Rev., 133, 1224–1240.

Nguyen, A., D. Menemenlis, and R. Kwok, 2008: Improved modeling of the Arctic
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Ponte, R. M., C. Wunsch, and D. Stammer, 2007: Spatial mapping of time-variable errors
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Figure 1: Adjoint sensitivity of cost function (monthly model/data quadratic misfit) to
the initial temperature field at the 15-m depth. Units are (°C)–1. The sensitivity is shown
after a 332-day adjoint integration. The domain of integration is a global cubed sphere
with 18-km horizontal grid spacing, as described in the text. Red patches are regions
where the model-data misfit increases and blue patches are regions where the model/data
misfit decreases with increasing initial temperature. The adjoint sensitivities are a critical
step towards fully constraining this global ocean and sea-ice model with insitu and
remote sensing data.
Figure 2: Vertical temperature and salinity structures (upper panel) and T/S diagram
(lower panel) of the Canadian Basin in August 2003. In the upper panel, the actual CTD
observations are shown in light gray, with the data mean shown in dashed heavy black.
 Additional annotations are a) mixed layer, b) summer Pacific Water source, and c)
winter Pacific Water source water. Dashed contours in T/S diagram are density
anomalies. Blue lines (A0) are from the global ECCO2 solution while red lines (A1) are
from a regional optimization that includes a sub-grid-scale parameterization of salt
plumes (Nguyen et al., 2008).
Figure 3: Simulated melt rate of all Antarctic ice shelves in November 2002. Units are m
of ice per year. Positive values indicate refreezing and negative values indicate melting
of the ice shelves. The simulation was carried out by M. Schodlok on a regional,
Southern Ocean domain carved out from the global cubed-sphere ECCO2 domain of
Figure 1. The mean circumpolar freshwater input into the Southern Ocean from ice
shelves between 1992 and 2006 amounts to 59.8±7.4 mSv. This regional study is a step
towards incorporating realistic ice-shelf ocean interactions in the global ECCO2
Figure 4: Snapshot of near-surface (15-m depth) current speed on March 3, 1993 from a
quasi-global simulation with 1/16 th-degree horizontal grid spacing (horizontal grid
spacing is approximately 6.9 km at the Equator and 1.19 km at +/-80°). High-resolution
simulations are being used by the ECCO2 project to estimate model errors and to inform
sub-grid-scale parameterizations (Hill et al., 2007). This particular simulation is also
being used for testing a new supercomputer (Pleiades) at NASA Ames and in the design
of the Surface Water and Ocean Topography (SWOT), a Decadal-Survey-recommended
high-resolution altimeter mission.
Figure 5: First guess and residual misfit between observed and modeled SST after 26
iterations of minimization of the least squares misfit function. Observations are from
remotely-sensed passive microwave radiometry.
Figure 6: Atlantic Water rim currents in the Arctic Ocean from a constrained ECCO2
solution. Background color shows the bathymetry in km. Small arrows represent
velocity magnitudes (|u|>0.03m/s in black, 0.01<|u|<0.03m/s in dark gray, and
0.003<|u|<0.01m/s in light gray). Large white solid arrows show total Atlantic Water
transports as per scale provided in the legend. Large dashed white arrows are inferred
transports based on current strengths. The thick red lines denote the Fram Strait (FA) and
the St Ana Trough (SA). A fully constrained ECCO2 solution will permit more accurate
estimates of these currents, of their time evolution, and of their impact on Arctic climate
than is possible with data or with models alone.
Figure 7: Self-assembling marine ecosystem. ECCO2 results are being used to supply
boundary conditions for regional studies and to drive biogeochemical, geodetic, acoustic,
and electromagnetic models. For example, O. Jahn used ECCO2 results to drive a self-
assembling marine ecosystem, a shown in the figure above.

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